code: def identity_features_numpy(image, landmarks, return_aligned=False):
      src_img = np.array([image])
      src_lms = np.array([landmarks])
      src_img = tf.constant(src_img, tf.float32)
      src_lms = tf.constant(src_lms, tf.float32)
      aligned_img = align_arcface(src_img, src_lms)
      emb_norm, _, _, vars = identity_features(
          aligned_img, "input_id_features"
      )
      var_init = tf.variables_initializer(vars)
      with tf.Session() as sess:
          sess.run(var_init)
          features = sess.run(emb_norm)
          if return_aligned:
              aligned = sess.run(aligned_img)
  
      tf.reset_default_graph()
      if return_aligned:
          return features, aligned
      else:
          return features
  
file_path: insightface/reconstruction/ostec/core/arcface_handler.py
imports: import tensorflow
  import numpy
  from external import arcface50
  from skimage import transform
label_desc: Use image node_id 4 for calling a global function with example usage: identity_features_numpy(image, landmarks, return_aligned) and returns: features,  aligned, features